Annals of the rheumatic diseases · 2025
Performance analysis of a deep-learning algorithm to detect the presence of inflammation in MRI of sacroiliac joints in patients with axial spondyloarthritis.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Nicolaes Joeri, Tselenti Evi, Aouad Theodore, López-Medina Clementina, Feydy Antoine, Talbot Hugues, Hoepken Bengt, de Peyrecave Natasha, Dougados Maxime
Аннотация
OBJECTIVES: To assess the ability of a previously trained deep-learning algorithm to identify the presence of inflammation on MRI of sacroiliac joints (SIJ) in a large external validation set of patients with axial spondyloarthritis (axSpA).
METHODS: Baseline SIJ MRI scans were collected from two prospective randomised controlled trials in patients with non-radiographic (nr-) and radiographic (r-) axSpA (RAPID-axSpA: NCT01087762 and C-OPTIMISE: NCT02505542) and were centrally evaluated by two expert readers (and adjudicator in case of disagreement) for the presence of inflammation by the 2009 Assessment of SpondyloArthritis International Society (ASAS) definition. Scans were processed by the deep-learning algorithm, blinded to clinical information and central expert readings.
RESULTS: Pooling the patients from RAPID-axSpA (n=152) and C-OPTIMISE (n=579) yielded a validation set of 731 patients (mean age: 34.2 years, SD: 8.6; 505/731 (69.1%) male), of which 326/731 (44.6%) had nr-axSpA and 436/731 (59.6%) had inflammation on MRI per central readings. Scans were obtained from over 30 scanners from 5 manufacturers across over 100 clinical sites. Comparing the trained algorithm with the human central readings yielded a sensitivity of 70% (95% CI 66% to 73%), specificity of 81% (95% CI 78% to 84%), positive predictive value of 84% (95% CI 82% to 87%), negative predictive value of 64% (95% CI 61% to 68%), Cohen's kappa of 0.49 (95% CI 0.43 to 0.55) and absolute agreement of 74% (95% CI 72% to 77%).
CONCLUSION: The algorithm enabled acceptable detection of inflammation according to the 2009 ASAS MRI definition in a large external validation cohort.
METHODS: Baseline SIJ MRI scans were collected from two prospective randomised controlled trials in patients with non-radiographic (nr-) and radiographic (r-) axSpA (RAPID-axSpA: NCT01087762 and C-OPTIMISE: NCT02505542) and were centrally evaluated by two expert readers (and adjudicator in case of disagreement) for the presence of inflammation by the 2009 Assessment of SpondyloArthritis International Society (ASAS) definition. Scans were processed by the deep-learning algorithm, blinded to clinical information and central expert readings.
RESULTS: Pooling the patients from RAPID-axSpA (n=152) and C-OPTIMISE (n=579) yielded a validation set of 731 patients (mean age: 34.2 years, SD: 8.6; 505/731 (69.1%) male), of which 326/731 (44.6%) had nr-axSpA and 436/731 (59.6%) had inflammation on MRI per central readings. Scans were obtained from over 30 scanners from 5 manufacturers across over 100 clinical sites. Comparing the trained algorithm with the human central readings yielded a sensitivity of 70% (95% CI 66% to 73%), specificity of 81% (95% CI 78% to 84%), positive predictive value of 84% (95% CI 82% to 87%), negative predictive value of 64% (95% CI 61% to 68%), Cohen's kappa of 0.49 (95% CI 0.43 to 0.55) and absolute agreement of 74% (95% CI 72% to 77%).
CONCLUSION: The algorithm enabled acceptable detection of inflammation according to the 2009 ASAS MRI definition in a large external validation cohort.